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Abstract
Complexity perspectives have been proposed as a way of underpinning more nuanced, contextualized understandings for second language research and pedagogy. Notwithstanding, persistent voices maintain critical flaws in such an application. This article provides a response to such naysaying previously published in Second Language Research by Gabriele Pallotti. I address important questions raised about the language of complexity, ideas of reduction and representation, and aims of generalization and prediction for research. I clarify aspects of the criticisms made, whilst concurrently recognizing challenges evident in current applications of complexity. In essence, I admit that the language of complexity is at times abused, while contending there are clear reasons why we need reminding of the dynamic, contextualized and unique nature of much that we study; I assert that complexity does not rule out reduction and representation completely; and I contest that generalization and prediction should not be a gold standard for which to aim. Based in my primary role as a classroom teacher and practitioner researcher, I argue that complexity reminds us to adopt a focus on more contextualized understandings and maintain our recognition of the humanity of language, language use, and the people involved.
Keywords
complexity, epistemology, representation, generalization, prediction
Introduction
Ever since Larsen-Freeman’s (1997) seminal publication, complexity perspectives have been lapping at the edges of the pool of accumulated Second Language Development (SLD) understandings. That contribution promoted consideration of languages, their use and learning, and the people involved in these endeavors as complex systems dynamically interacting in a nonlinear fashion. Efforts at removing elements from time or their interconnected contexts were adjudged nonsensical. Thus, Larsen-Freeman (1997: 142) expressed the “hope that learning about the dynamics of complex nonlinear systems will discourage reductionist explanations in matters of concern to second language acquisition researchers”. Ecological approaches (e.g., Kramsch, 2002; Kramsch & Steffensen, 2008; van Lier, 2004) and elements of the transdisciplinary framework for SLD proposed by The Douglas Fir Group (2016) equally have aspects in common with complexity thinking. Indeed, the years subsequent to Larsen-Freeman’s (1997) entreaty have seen numerous complexity-inspired publications, such as theoretical and methodological books (e.g., Hiver & Al-Hoorie, 2020; King, 2016; Larsen-Freeman & Cameron, 2008; Sampson & Pinner, 2021; Verspoor et al., 2011), empirical monographs in second language (L2) education (e.g., Kostoulas, 2018; Pinner, 2019; Sampson, 2016a), research articles dealing with a variety of areas within SLD (e.g., De Bot et al., 2007; Henry, 2016; Hiver & Al-Hoorie, 2016; Mercer, 2011; Sampson, 2016b; van Geert, 2008) and attempts to describe how complexity perspectives might inform language teaching (e.g., Mercer, 2013; Pinner & Sampson, 2021).
Nevertheless, there remain strongly dissenting voices, with Pallotti’s (2021: 11) article raising a number of points that mean, in his view, complexity “risks becoming a flag under which to fight not very productive academic crusades”. This article aims to add to this discussion by both clarifying aspects of certain of the claims made in such naysaying, whilst concurrently recognizing some current challenges to complexity research and theorizing. Given my own role as a practitioner researcher (rather than an academic or theoretician), the stance that I proffer draws parallels between complexity thinking and a more humanistic consideration of (the people involved in) SLD. In contrast to objections raised by influential voices (Ellis, 2021) that complexity offers little of practical use for an applied field like SLD, my viewpoint thus in fact stems from my practical experiences in language classrooms.
On the language of complexity
In his thought-provoking piece, Pallotti (2021) draws parallels between complexity perspectives and an Athenian philosopher, Cratylus. According to Pallotti’s reading, Cratylus interpreted Heraclitean philosophy (that ‘everything flows’) to such an extreme degree that he felt it impossible to speak, for “words themselves ‘freeze’ reality and offer a static, reductionist representation of it” (Pallotti, 2021: 2). Pallotti makes the case that complexity perspectives equally allow us to say or do very little of practical use. One example of this claim is his assertion that complexity displays “Cratylism in theoretical statements” (Pallotti, 2021: 4). By this, he means that much of the writing about complexity is rather loquacious whilst at the same time being content-impoverished. In his view, “affirming that everything is dynamic and complex is an example of a metaphysical statement that cannot be falsified and with a surprise value close to zero”, while “it is equally obvious that all entities are different” (Pallotti, 2021: 4).
Perhaps it may be the fault of writers founding their research on complexity principles for not sufficiently rehashing past research landscapes. It seems the point they (we) wish to make is that much empirical work has been turning a blind eye to such aspects for decades. Take one area close to my heart: Drawing on their vast experience, MacIntyre et al. (2021: 22) reflect that “quantitative research projects in the psychology of language learning are most often done with a cross-sectional design…using a pre-determined set of instruments…while collecting data in as large a sample as possible”. However, as van Dijk et al. (2011: 62) point out:
…if we really want to know how an individual (or group) develops over time we need data that is dense (i.e. collected at many regular measurement points), longitudinal (i.e. collected over a longer period of time), and individual (i.e. for one person at a time and not averaged out).
Surely, SLD should concern itself with such development over time? As Pallotti contends, many of the properties of complexity do indeed seem “obvious”. Yet, given that many past efforts to study something as dynamic, complex, and individualized as processes of language development have ignored such qualities, writers may feel it necessary to reiterate.
All this said, I must admit there are pitfalls intrinsic to the language of complexity. These challenges relate to the problem of how complexity has been ‘packaged’ or ‘branded’ through articles, books and conferences. It seems we may have expended so much time and so many words trying to show complexity has something novel to offer SLD that occasionally we lose sight of the wood for the trees. Complexity thinking is a philosophical viewpoint that reminds us of certain ways of thinking about the world (Morin, 2008). Indeed, Larsen-Freeman (2017) refers to it as a metatheory; it extends epistemological, ontological and axiological guidance. Put another way, it furnishes a “conceptual framework that provides broad theoretical and methodological principles for how to judge what is meaningful (or not), acceptable (or not), and central (or not) in the task of building knowledge about a phenomenon” (Ortega & Han, 2017: 2-3). Lamentably, writing underpinned by complexity perspectives can tend to obfuscate possibilities for illuminating insights with an excess of technical terms such as “emergence”, “self-organization”, “nonlinearity” or “openness” – even while the concepts themselves might be quite familiar to most involved in SLD. Another frequent flaw is a mistaken belief that in order to align oneself with complexity thinking, everything needs to be described as a ‘system’. The following quote supplies a fitting example: “It must be noted that while…the L2 developmental system is closed rather than open, individual learners should strive to keep their systems as open as possible” (Han, Bao, & Wiita, 2017: 227 – emphasis added). I imagine that if I urged the young adults in my classrooms to “keep their systems as open as possible”, I would be met with widespread puzzlement. Alas, I also am not free from blame here. For instance, as I described the behaviors of a learner in one of my previous publications: “His attempts at action in the current system met with success, positively reinforcing that this kind of action would be appropriate in this system” (Sampson, 2016a: 128 – emphasis added). Clearly, I was talking about a group of which this student was a member, yet my insistence on using the word “system” in an attempt to maintain a complex focus was misguided and unnecessarily opaque. In response to Pallotti’s (2021) concerns about the language of complexity, then, perhaps we ought to strive to leave the jargon at the doorstep and instead show via our research interpretations and representations what complexity can add to the SLD landscape.
On reduction and representation
Regarding the conduct of research, Pallotti (2021) vigorously defends quantitative research methods and the utility of reduction. As he opines:
The fact that the average, or a regression line, do not correspond to any particular data point is not a limit of these relatively simple models, nor an issue to wonder about; rather, these models help us to solve concrete problems, like that of making sense of a number of sparse observations. (Pallotti, 2021: 5)
Morin (2008) contends that we have been socialized in a mechanistic view in which we believe that we can remove any part from the machine to understand its function and workings or view the parts as fundamentally similar and average across them. Aligning himself with a complexity perspective, he thus argues that our thinking is founded on “the principle of simplicity [which] either separates that which is linked (disjunction), or unifies that which is diverse (reduction)” (Morin, 2008: 39). Admittedly, averages (reduction) have their uses. Reflecting once more on the study of language learning psychology, large-scale research that relied on averages etched out the empirical interest in language anxiety (Horwitz, Horwitz and Cope, 1986). This focus then afforded teachers more general understandings of the potential causes and impact of such an emotion (see MacIntyre, 2017, for an overview). The reduction here also, however, meant that for years research fixated on this one area, without taking note of the brilliant diversity of emotionality in language learning (see Pavlenko, 2013, for criticism). Nevertheless, (Pallotti, 2021: 3) goes to great lengths spelling out the benefits of reduction, as in the following:
In many cases reducing complexity and dynamism may have positive effects. Suppose you arrive in a city you have never visited before. Getting off the train, who would you prefer to meet: someone offering you a lecture on the infinite complexity of this city, its being a system made up of billions of particles in continuous motion, interacting with one other [sic], and whose behavior can never be predicted exactly, or someone handing you a very simple map? In other words, the map is not the territory, but a reduction of it, and this is not a weakness of the map, but one of its strengths and design features.
That is, one of the consistent threads running through Pallotti’s (2021) article is an insistence that complexity thinking discards reduction in toto, that it disavows any kind of (reduced) representation. In this regard, there is a body of literature devoted to questions of presentation and representation given complexity understandings (e.g., Cilliers, 1998; Davis & Sumara, 2006; Osberg et al., 2008). In refutation of Pallotti’s arguments, Osberg et al. (2008: 208) contest complexity “does not imply that we should attempt to do without representations, but that we need to rethink the status and the purpose of our representations”. Complexity reminds us that the act of representation is always an act of reduction. Whilst trying our best to “include context as part of the system(s) under investigation” and “honor the complexity by…avoid[ing] premature idealization” (Larsen-Freeman & Cameron, 2008: 241-242), we work to arrive at representations that are adequate for what we are trying to understand. Complexity also reminds us to be humble about what we are claiming: “Our understanding of complex phenomena is never perfect” and thus representations ought to be understood as “valuable but provisional and temporary tools by means of which we constantly re-negotiate our understanding of and being in the world” (Osberg et al., 2008: 208 – emphasis in original).
It might further be contended that arguments of the benefits of reduction analogized by a map are flawed in many respects. In the first case, a map is based on geographical and spatial features which are themselves unique to a particular location; for instance, we cannot extrapolate from a map to all towns with a castle or all roads leading thereto. Equally, and recollecting Pallotti’s (2021: 5) assertion that averages help us to “make sense of a number of sparse observations”, the specific information from a local that recently there is an especially mean-looking crocodile inhabiting one of the roads of an evening might prove quite vital (but not be included on a representation such as a map, and certainly not in a generalized version). That is, sometimes (often), we need to care about particular data points in their interactions with context and within time (and surely all the more so when considering humans and their languages).
One of the key tenets of complexity thinking is that phenomena (such as meaning) emerge via the interaction of parts. Taking the parts out of the richness of their context tells us little, as does averaging across the parts. It is the parts interacting in certain ways, with certain qualities, at certain times that gives rise to a particular phenomenon. Equally, the whole adapts the parts (Larsen-Freeman & Cameron, 2008) and things change – different interactions of the parts as well as different qualities of the parts will give rise to different emergent phenomena.
The key point is that contextualized understandings are vital. As Simpson and Rose (2021: 138) remark, “if a biologist were aiming to understand how a certain type of plant grew simply by observing the plant, but failed to consider either the growing medium, or nutrients, they would be neglecting a significant element of what fosters plant growth”. Complexity does not mean that averages and “relatively simple models” (Pallotti, 2021: 5) must be thrown out the window, but it does remind us to be (more) aware of the importance of the interconnectedness of the phenomena we are studying. Additionally, as SLD considers people and their languages, complexity prompts us to remain cognizant of the unique human experiences involved in languages and their acquisition. Even work underpinned by complexity foundations can move to the abstract, attempting to develop mathematical expressions reducing complex systems to fundamental principles (see, e.g., Lowie, 2017, for discussion). Yet, such moves work to “decomplexify” (Morin, 2007: 10) and dehumanize the phenomenon under investigation. In our own field, Ushioda (2021: 274) asserts that
such discussions of human behaviour can create, as Larsen-Freeman and Cameron (2008: 74) openly admit, something of a “distancing” effect, where individual intentionality, reflexivity and decision-making become transmuted into mathematical models representing abstract systems above the level of the individual person. In a sense, it is as if the abstract self-organizing system has a theoretical life of its own, and we lose sight of the people themselves and their lived experiences and local realities.
While we cannot possibly include everything, we need to find appropriate levels of reduction and representation that do justice to the human experience of language. At a recent discussion of conducting SLD research underpinned by complexity thinking, one participant described such an endeavor thus: We start with a sauce in which a multitude of ingredients with their distinctive flavors are apparent, and we simmer the sauce down to just the right taste. Not enough, and we may be overwhelmed by the competing flavors and unable to make sense of the sauce; but too much, and we may lose all sense of cinnamon (Simpson, March 6, 2021 personal communication).
On generalization and prediction
As we have just seen, adhering to complexity thinking, understandings of phenomena are considered interpretive and exploratory, rather than explanatory or predictive (Alhadeff-Jones, 2013). One of Pallotti’s (2021: 5) vital criticisms of SLD taken from a complexity perspective is that such a descriptive nature means researchers refrain from making strong (generalizable) claims, “saying without saying too much”. As he continues:
In many areas of science that CDST calls ‘traditional’, description is seen as a first step followed by generalization and prediction. …It sounds rather odd that an approach aimed at expanding applied linguistics’ resources and methodologies can be ‘content’ to do less than what is done in ‘more traditional’ research. (Pallotti, 2021: 6-7)
A key voice in SLD theorizing for years, Ellis (2021: 200) concurs:
I have my doubts about this theory, not least because it offers no predictions about L2 acquisition and resists generalization. …if [SLD] is an applied discipline, then surely there is a need for generalizations that can inform applications. What does Complex Dynamic Systems Theory have to say to teachers, for example? Telling teachers that language learning is complex, idiosyncratic and unpredictable might be helpful in developing their awareness of the nature of L2 learning but it does not offer any practical suggestions about how to the [sic] design and implement language instruction.
As Larsen-Freeman (2015) admits, some (simplistic, linear) phenomena are represented with a Gaussian distribution as a bell curve (and hence can be generalized and predicted to a certain extent). Yet, complex, nonlinear phenomena may also have non-Gaussian distributions, meaning that “infrequent behaviour at the edge of a bell curve is much more common” and thus “computing the average behaviour does not tell us much about the behaviour of the components or agents” (Larsen-Freeman, 2015: 17). Such nonlinearity implies that “the really relevant elements – the triggers of large-scale change for instance – can be exceptional, deviant and statistically insignificant” (Blommaert, 2014: 16). In such a sense, while in SLD “stages of development…are commonly observed as a grand sweep effect at the group level, these stages may be meaningless at the level of the individual learner” (Lowie & Verspoor, 2015: 63). Is, as Pallotti (2021: 7) would urge, acknowledging such processes “do[ing] less”?
Pallotti (2021: 5) nevertheless asserts that “admonitions on the risks and difficulties of predicting and generalizing don’t offer a particularly relevant contribution, as everybody is aware of these difficulties”. Why then do many who add to the empirical (and theoretical) landscape persist as if unaware of such risks and difficulties and instead make prediction a gold standard? Is it reasonable to continue making simplistic generalizations and predictions if we recognize complexity? Should we not appreciate the unique humanity of the people involved in language learning and use? To what degree do abstract generalizations built around simplistic assumptions truly “offer any practical suggestions” (Ellis, 2021: 200) for teachers in classrooms with particular language learners with their particular complexity?
In terms of my own area of study – practitioner research into the psychologies and sociality of the people in my classrooms – complexity encourages us to look (really look!) at phenomena of interest in specific contexts in time, describe, and try to understand what is going on in their emergence. And, if I understand my particular context more deeply, I may be enabled to make more intelligently-informed decisions about how I might foster conditions more useful for learning. In this sense, complexity does not preclude acting for the future, it merely (?) suggests we think about tendencies for action in particular contexts with particular people, whilst recognizing that with everything going on we are never going to be able to make hard-and-fast predictions for all contexts or all people. While I might not be able to make widespread generalizations or predictions to populations, I can generalize to theoretical propositions and potentials (Larsen-Freeman, 2017). As Larsen-Freeman and Cameron (2008: 236) conclude:
This is not to say that the findings in one context in one study cannot be relevant to another, but saying they are relevant is not claiming some cause will produce the same effect. Rather, it is saying that what generalizes from one context to another is the need to look at dynamic interactions and the possibility of connected emergent outcomes. In other words, what generalizes are the mechanisms and dynamics of complex systems.
Conclusion
Pallotti’s (2021) timely position piece has raised various concerns about the usefulness of complexity-informed research and theorizing going forward. While his main criticisms revolve around questions of language, reduction and representation, and generalization and prediction in complexity perspectives, I hope to have clarified some (mis)understandings in these points. Notwithstanding, I do agree with one of his conclusions, that “[complexity] research is now at a crossroads” (Pallotti, 2021: 10). We need to take great care that the sometimes opaque vocabulary of complexity and creation of ever more abstract representations do not take center stage, distracting us from maintaining a practical purpose to our research and keeping our focus on real people and the languages they employ. In this respect, from my perspective as a classroom practitioner who deals in complexity day in and day out, I hold high hopes for the usefulness of complexity in furnishing more nuanced, humanistic understandings of second language development. And while, as I hope to have made clear, prediction is not something we should (or can) aim for given complexity, I do see that complexity might add much to understandings of underlying processes involved in learning and using languages. Hence, rather than expecting the construction of complexity-driven theories of language, use, and learning, we might better anticipate complexity-informed understandings and approaches (Mahmoodzadeh, March 17, 2021, personal communication).
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